Identification of Active Subfunctions in Finite-Max Minimisation via a Smooth Reformulation
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913316581933056 |
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| author | Ras, Charl Tam, Matthew Uteda, Daniel |
| author_facet | Ras, Charl Tam, Matthew Uteda, Daniel |
| contents | In this work, we consider a nonsmooth minimisation problem in which the objective function can be represented as the maximum of finitely many smooth ``subfunctions''. First, we study a smooth min-max reformulation of the problem. Due to this smoothness, the model provides enhanced capability of exploiting the structure of the problem, when compared to methods that attempt to tackle the nonsmooth problem directly. Then, we present several approaches to identify the set of active subfunctions at a minimiser, all within finitely many iterations of a first order method for solving the smooth model. As is well known, the problem can be equivalently rewritten in terms of these subfunctions, but a key challenge is to identify this set \textit{a priori}. Such an identification is clearly beneficial in an algorithmic sense, since one can apply this knowledge to create an equivalent problem with lower complexity, thus facilitating generally faster convergence. Finally, numerical results comparing the accuracy of each of these approaches are presented, along with the effect they have on reducing the complexity of the original problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10326 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identification of Active Subfunctions in Finite-Max Minimisation via a Smooth Reformulation Ras, Charl Tam, Matthew Uteda, Daniel Optimization and Control 49K35, 65B99, 65K15, 65Y20, 90C25, 90C33, 90C47 In this work, we consider a nonsmooth minimisation problem in which the objective function can be represented as the maximum of finitely many smooth ``subfunctions''. First, we study a smooth min-max reformulation of the problem. Due to this smoothness, the model provides enhanced capability of exploiting the structure of the problem, when compared to methods that attempt to tackle the nonsmooth problem directly. Then, we present several approaches to identify the set of active subfunctions at a minimiser, all within finitely many iterations of a first order method for solving the smooth model. As is well known, the problem can be equivalently rewritten in terms of these subfunctions, but a key challenge is to identify this set \textit{a priori}. Such an identification is clearly beneficial in an algorithmic sense, since one can apply this knowledge to create an equivalent problem with lower complexity, thus facilitating generally faster convergence. Finally, numerical results comparing the accuracy of each of these approaches are presented, along with the effect they have on reducing the complexity of the original problem. |
| title | Identification of Active Subfunctions in Finite-Max Minimisation via a Smooth Reformulation |
| topic | Optimization and Control 49K35, 65B99, 65K15, 65Y20, 90C25, 90C33, 90C47 |
| url | https://arxiv.org/abs/2404.10326 |